Fine-Tuning vs RAG: Understand the Difference in AI Approaches
AI has reached an unprecedented level of sophistication, with constant fine-tuning and evolution of models yielding greater adaptability to specific tasks and domains. In this article, we are comparing two of these AI models, LLM fine-tuning vs RAG (Retrieval-Augmented Generation).
Although they share similarities, the foundational concepts and applied nuances that shape them are firmly distinct, marking their unique relevance in domain and industry-specific fields.
LLM Fine-Tuning and RAG Explained
Both RAG and LLM Fine-Tuning fall under the wider umbrella of AI approaches geared to improve the way an AI model responds to user inputs. The aim is to increase the models’ understanding of complex user requests, improve their relevance, and personalize their responses.
These models are built on a transformer architecture, typically pre-trained on large and diverse datasets. This pre-training helps develop a broad understanding of language structure, context, and overall semantics.
What is LLM Fine-Tuning? How does it work & why is it important?
The principle underpinning LLM fine-tuning is based on taking a pre-trained base model and further teaching it using a specialized corpus, which is smaller, domain-relevant, and task-specific. During this phase, slight adjustments are made to the model’s internal parameters (weights) to reduce the error in predictions in the new, specific context.
The learning rate during this stage is usually smaller than in pre-training to avoid drastic changes that could wipe out general language understanding acquired in the initial training phase (only a handful of internal model weights are subject to be adjusted).
For instance, consider the case of leveraging a large language model in the healthcare domain. A general large language model might have a basic understanding of medical terminology but might not have a detailed grasp of disease classifications, treatment protocols, drug interactions, etc. When we perform LLM fine-tuning with healthcare-specific data, the large language model can learn these specific terminologies.
For example, when the phrase “MI” is input, a general LLM might not understand that in the healthcare field, this often refers to “Myocardial Infarction”, also known as a heart attack. However, healthcare fine-tuned large language models would understand this reference and could provide information or support a conversation about diagnosis, treatment, risk factors, etc.
With LLM fine-tuning, the model gains a deeper understanding of the language nuances and mannerisms that are unique to the context. It transforms the general model into a tailored conversationalist able to handle user queries relative to a specific domain or task with high precision.
What is RAG? How Does It Work and Why Is It Important?
On the other hand, RAG combines aspects of retrieval-based systems and generative models. The RAG model recognizes that no matter how comprehensive the training data is for a general LLM, there’s always potential for missing data elements that could contribute to answering new, complex questions.
This is particularly relevant when new public knowledge emerges after the model has been trained, or when proprietary enterprise information (which is naturally private and therefore not included in the general model training) is needed to provide an answer. To overcome this, RAG fetches relevant information from a more complete document set in real time and incorporates it into its responses.
With RAG, a higher level of context-specific insight and understanding is achieved. The RAG model acts as the instant expert that evolves a general large language model into a specialized one, capable of retrieving and utilizing relevant information to provide precise responses, even to queries that require knowledge beyond its initial training data
Supercharge LLM Performance with RAG and LLM Fine-Tuning
To illustrate the benefits of combining RAG with LLM Fine-Tuning, let’s consider the case of a Generative AI Virtual Assistant trained to answer questions related to the healthcare domain. Let’s consider the following user question as an example: “What’s the effect of drug X on my hypertension and diabetes?”
An LLM fine-tuned with medical knowledge understands that “hypertension” and “diabetes” are chronic diseases, and “drug X” is a medicinal variable that can have an impact on these. The fine-tuned large language model can provide information like “drug X is commonly prescribed for condition Y, which is unrelated to hypertension and diabetes.” This specificity of contextual perception demonstrates the advantage of a fine-tuned LLM.
However, the user query also implicitly seeks information about potential interactions between drug X and their existing hypertensive and diabetic medications, a complex question that demands extensive retrieval of healthcare data. Even with the best LLM strategy, this task can exceed a fine-tuned LLM’s capacity, underscoring a limitation of this model.
The RAG approach leverages a vast database of healthcare information that includes details regarding drug interactions. For the same user query, the RAG might extract relevant information about “drug X”’s pharmacodynamics, its potential effect on diabetes and hypertension, and any known interactions with other medicines.
However, the RAG model could potentially face limitations when the query involves specialized terms or abbreviations common in healthcare. If “drug X” was a specific medical term unfamiliar to the system, the answer generation might be compromised, showing a limitation in its ability to decode highly specific medical abbreviations.
By marrying the benefits of a fine-tuned LLM with improved LLM performance and RAG, a holistic approach emerges that could address the user query more comprehensively. The fine-tuned LLM component recognizes “drug X”, hypertension, and diabetes plus their related terminologies or abbreviations and anchors the context accordingly.
Meanwhile, the RAG model retrieves relevant information about “drug X”, its effects on hypertension and diabetes, and any contraindications for patients with these medical conditions, providing a detailed, nuanced response to the user’s request.
Combining both models effectively navigates the sophisticated interplay between specialized medical vocabulary and the need for in-depth healthcare information recall. This enhanced UX better addresses the complexity and unique requirements of healthcare domain inquiries.
RAG and LLM fine-tuning: Better together!
In conclusion, both the Retrieval-Augmented Generation (RAG) and LLM Fine-Tuning models hold significant potential within specific domains and industries. They are well-equipped to handle complex user queries and can personalize responses based on context and domain-specific nuances.
By combining the benefits of the RAG’s ability to retrieve relevant information in real-time and the large language model fine-tuning’s deeper understanding of language peculiarities of the context, the user can receive precise and comprehensive responses to their inquiries.
Implementation of such a combination can prove to be extremely effective as a tool for providing a better user experience and effectively addressing the complexity and unique requirements of industry-specific inquiries.
This approach allows for surpassing individual limitations of the respective models, offering expertise in situations demanding extensive retrieval of specific data or understanding of specialized terms. Book an AI demo and explore Aisera’s Enterprise LLM solution for your organization today!